[v4.0] Make a bunch of updates to the docs.

docs/source/FAQ/predictor_evaluation_migration.rst

@@ -5,7 +5,7 @@ Migrating to Predictor Evaluations
 Summary
 =======
 
-In version 4.0, :py:class:`Predictor Evaluation Workflows <citrine.resources.predictor_evaluation_workflow.PredictorEvaluationWorkflowCollection>` and :py:class:`Predictor Evaluation Executions <citrine.resources.predictor_evaluation_execution.PredictorEvaluationExecutionCollection>` (collectively, PEWs) will be merged into a single entity called :py:class:`Predictor Evaluations  <citrine.resources.predictor_evaluation.PredictorEvaluationCollection>`. The new entity will retain the functionality of its predecessors, while simplyfing interactions with it. And it will support the continuing evolution of the platform.
+`Predictor Evaluation Workflows` and `Predictor Evaluation Executions` (collectively, PEWs) have been merged into a single entity called :py:class:`Predictor Evaluations <citrine.resources.predictor_evaluation.PredictorEvaluationCollection>`. The new entity retains the functionality of its predecessors, while simplyfing interactions with it, and supporting the continuing evolution of the platform.
 
 Basic Usage
 ===========

docs/source/data_extraction.rst

@@ -102,7 +102,7 @@ Assume that the process template is accessible from a known Project, ``project``
 
     table_config = table_config.add_all_ingredients(
         process_template=LinkByUID('id', '3a308f78-e341-f39c-8076-35a2c88292ad'),
-        project=project,
+        team=team,
         quantity_dimension=IngredientQuantityDimension.MASS
     )
 

docs/source/getting_started/ai_modules.rst

@@ -41,4 +41,4 @@ They also include a :doc:`Score <../workflows/scores>` which codifies goals of t
 Predictor Evaluation
 ^^^^^^^^^^^^^^^^^^^^
 
-:doc:`Predictor Evaluations <../workflows/predictor_evaluation_workflows>` analyze the quality of a Predictor.
+:doc:`Predictor Evaluations <../workflows/predictor_evaluations>` analyze the quality of a Predictor.

docs/source/getting_started/basic_functionality.rst

@@ -59,18 +59,18 @@ Similarly, the ``wait_while_executing`` function will wait for a design or predi
 Checking Status
 ---------------
 
-After registering an asset, the ``status`` command can be used to obtain a static readout of the state of the asset on the platform (e.g., VALID, INVALID, VALIDATING, SUCCEEDED, FAILED, INPROGRESS). 
+After registering an asset, the ``status`` command can be used to obtain a static readout of the state of the asset on the platform (e.g., READY, INVALID, VALIDATING, SUCCEEDED, FAILED, INPROGRESS). 
 
 .. code-block:: python
 
     sintering_model = sintering_project.predictors.register(sintering_model)
     sintering_model.status
 
-The ``status_info`` command returns additional details about an asset's status that can be very useful for debugging.
+The ``status_detail`` command returns additional details about an asset's status that can be very useful for debugging.
 
 .. code-block:: python
 
-    sintering_model.status_info
+    sintering_model.status_detail
 
 Reading
 -------

docs/source/getting_started/code_examples.rst

@@ -38,17 +38,17 @@ Note that all resources are given descriptive names and summaries.
     print("My new team has name {} and id {}".format(
         band_gaps_team.name, band_gaps_team.uid))
 
-    Strehlow_Cook_description = "Band gaps for elemental and binary " \
+    strehlow_cook_description = "Band gaps for elemental and binary " \
         "semiconductors with phase and temperature of measurement. DOI 10.1063/1.3253115"
-    Strehlow_Cook_dataset = Dataset(name="Strehlow and Cook",
-        summary="Strehlow and Cook band gaps", description=Strehlow_Cook_description)
-    Strehlow_Cook_dataset = band_gaps_team.datasets.register(Strehlow_Cook_dataset)
+    strehlow_cook_dataset = Dataset(name="Strehlow and Cook",
+        summary="Strehlow and Cook band gaps", description=strehlow_cook_description)
+    strehlow_cook_dataset = band_gaps_team.datasets.register(strehlow_cook_dataset)
 
 Find an existing Team and Dataset
 ------------------------------------
 
 Often you will work with existing resources.
-The code below retrieves a Team with the name "Copper oxides team" and a dataset with a known unique id that is stored as ``dataset_A_uid``.
+The code below retrieves a Team with the name "Copper oxides team" and a dataset with a known unique id that is stored as ``dataset_a_uid``.
 For more information on retrieving resources, see :ref:`Reading Resources <functionality_reading_label>`.
 
 .. code-block:: python
@@ -58,7 +58,7 @@ For more information on retrieving resources, see :ref:`Reading Resources <funct
     copper_oxides_team = next((team for team in all_teams
         if team.name == team_name), None)
     assert copper_oxides_team is not None
-    dataset_A = copper_oxides_team.datasets.get(uid=dataset_A_uid)
+    dataset_a = copper_oxides_team.datasets.get(uid=dataset_a_uid)
 
 Find a template
 ---------------

docs/source/getting_started/datasets.rst

@@ -21,7 +21,6 @@ Datasets can be listed and gotten from Teams just as Teams are from the client i
 
     team = citrine.teams.get("baaa467e-1758-43a8-97c7-76e569d0dcab")
     datasets_in_team = team.datasets.list()
-    a_dataset = project.datasets.get("6ed8bf3b-876b-40f0-9d50-982e686e5cd3")
 
 Datasets can only be deleted if they are empty.
 If you need to delete a non-empty Dataset, see this discussion on :ref:`deleting data objects <deleting_data_objects_label>`.
@@ -47,11 +46,11 @@ Assume you have a "band gaps team" with known id, ``band_gaps_team_id``, and are
     band_gaps_team = citrine.teams.get(band_gaps_team_id)
 
     # create the Dataset object
-    Strehlow_Cook_description = "Band gaps for elemental and binary semiconductors with phase and temperature of measurement. DOI 10.1063/1.3253115"
-    Strehlow_Cook_dataset = Dataset(name="Strehlow and Cook", summary="Strehlow and Cook band gaps", description=Strehlow_Cook_description)
+    strehlow_cook_description = "Band gaps for elemental and binary semiconductors with phase and temperature of measurement. DOI 10.1063/1.3253115"
+    strehlow_cook_dataset = Dataset(name="Strehlow and Cook", summary="Strehlow and Cook band gaps", description=strehlow_cook_description)
 
     # pass the Dataset object to the registration endpoint
-    Strehlow_Cook_dataset = band_gaps_team.datasets.register(Strehlow_Cook_dataset)
+    strehlow_cook_dataset = band_gaps_team.datasets.register(strehlow_cook_dataset)
 
 
 Deleting a Dataset

docs/source/getting_started/projects.rst

@@ -5,7 +5,7 @@ Projects
 A Project is the basic container for AI Assets on the Citrine Platform, such as GEM Tables, Predictors, Design Spaces, and Design Workflows.
 Access rights on resources inside a Project are managed, granted, and revoked at the Team level.
 
-Users are individuals using the Citrine Platform, and they are made members of Team.
+Users are individuals using the Citrine Platform, and they are made members of a Team.
 A user who is a member of a Team has access to all of the Projects that the Team has access to.
 
 Every interaction with every other type of resource is scoped to a single Team.
@@ -33,8 +33,15 @@ To retrieve a Project in the team, either find the Project in the list:
 
     project_name = "Copper oxides project"
     all_projects = team.projects.list()
-    copper_oxides_project = next((project for project in all_projects
-    if project.name == project_name), None)
+    project = next((project for project in all_projects if project.name == project_name), None)
+
+or use the :func:`~citrine.seeding.find_or_create.find_or_create_project` convenience method:
+
+.. code-block:: python
+
+    from citrine.seeding.find_or_create import find_or_create_project
+    project_name = "Copper oxides project"
+    project = find_or_create_project(project_collection=team.projects, project_name=project_name)
 
 or get it by unique identifier:
 

docs/source/workflows/code_examples.rst

@@ -79,8 +79,7 @@ This pattern is also extremely useful for performing optimization over complex o
         outputs = [
             final_ph,
             final_loaf_hydration,
-        ],
-        training_data=[training_table]
+        ]
     )
 
     shelf_life_calculator = ExpressionPredictor(
@@ -104,7 +103,8 @@ This pattern is also extremely useful for performing optimization over complex o
             dough_hydration_calculator,
             physical_properties_predictor,
             shelf_life_calculator
-        ]
+        ],
+        training_data=[training_table]
     )
 
 .. |Bread Predictor Graph Visualization| image:: bread_predictor_graph_viz.jpg

docs/source/workflows/data_sources.rst

@@ -19,25 +19,31 @@ The example below assumes that the uuid and the version of the desired GEM Table
 .. code:: python
 
     from citrine.informatics.data_sources import GemTableDataSource
-    from citrine.informatics.predictors import AutoMLPredictor
+    from citrine.informatics.predictors import AutoMLPredictor, GraphPredictor
     from citrine.informatics.descriptors import RealDescriptor, CategoricalDescriptor, ChemicalFormulaDescriptor
 
     data_source = GemTableDataSource(
         table_id = "842434fd-11fe-4324-815c-7db93c7ed81e",
         table_version = "2"
     )
 
-    predictor = AutoMLPredictor(
+    auto_ml_predictor = AutoMLPredictor(
         name = "Band gap predictor",
         description = "Predict the band gap from the chemical formula and crystallinity",
         inputs = [
             ChemicalFormulaDescriptor("terminal~formula"),
             CategoricalDescriptor("terminal~crystallinity", categories=[
                 "Single crystalline", "Amorphous", "Polycrystalline"])
         ],
-        outputs = [RealDescriptor("terminal~band gap", lower_bound=0, upper_bound=20, units="eV")],
+        outputs = [RealDescriptor("terminal~band gap", lower_bound=0, upper_bound=20, units="eV")]
+    )
+
+    predictor = GraphPredictor(
+        name = "Root predictor",
+        predictors = [auto_ml_predictor],
         training_data = [data_source]
     )
+
 
 Note that the descriptor keys above are the headers of the *variable* not the column in the table.
 The last term in the column header is a suffix associated with the specific column definition rather than the variable.

docs/source/workflows/descriptors.rst

@@ -7,7 +7,7 @@ Descriptors allow users to define a controlled vocabulary with which to describe
 Each descriptor defines a term in that vocabulary, which is comprised of a name, a datatype, and bounds on that data type.
 If you are familiar with the GEMD data model, descriptors are roughly equivalent to :class:`AttributeTemplates <citrine.resources.attribute_templates.AttributeTemplate>`.
 
-The AI Engine currently supports 5 kinds of descriptors:
+The AI Engine currently supports 6 kinds of descriptors:
 
 -  `Real Descriptors <#real-descriptor>`__
 -  `Integer Descriptor <#integer-descriptor>`__